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Dockingapp: a User Friendly Interface for Facilitated Docking Simulations with Autodock Vina

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Dockingapp: a User Friendly Interface for Facilitated Docking Simulations with Autodock Vina J Comput Aided Mol Des DOI 10.1007/s10822-016-0006-1 DockingApp: a user friendly interface for facilitated docking simulations with AutoDock Vina Elena Di Muzio1 · Daniele Toti1 · Fabio Polticelli1 Received: 4 November 2016 / Accepted: 22 December 2016 © Springer International Publishing Switzerland 2017 Abstract Molecular docking is a powerful technique Keywords Molecular docking · Virtual screening · Drug that helps uncover the structural and energetic bases of repurposing · Graphic interface · Wrapper · AutoDock Vina the interaction between macromolecules and substrates, endogenous and exogenous ligands, and inhibitors. Moreo- ver, this technique plays a pivotal role in accelerating the Introduction screening of large libraries of compounds for drug devel- opment purposes. The need to promote community-driven Collective efforts, such as the Drugs for Neglected Dis- drug development efforts, especially as far as neglected dis- eases initiative [7], can dramatically speed up the devel- eases are concerned, calls for user-friendly tools to allow opment of novel drugs at a significantly lower cost. Fur- non-expert users to exploit the full potential of molecular thermore, repurposing of Food and Drug Administration docking. Along this path, here is described the implemen- (FDA)-approved drugs allows to bypass the expensive and tation of DockingApp, a freely available, extremely user- time-consuming toxicity assays and clinical trials, greatly friendly, platform-independent application for perform- reducing the time needed to bring a repurposed drug to the ing docking simulations and virtual screening tasks using market. In this framework, docking simulations play a cen- AutoDock Vina. DockingApp sports an intuitive graphical tral role in the drug development pipeline. However, dock- user interface which greatly facilitates both the input phase ing techniques are not readily accessible to researchers out- and the analysis of the results, which can be visualized in side the structural bioinformatics field, and therefore their graphical form using the embedded JMol applet. The appli- potential cannot be fully exploited in community-driven cation comes with the DrugBank set of more than 1400 drug discovery initiatives. In the attempt of overcoming the ready-to-dock, FDA-approved drugs, to facilitate virtual technical difficulties of docking simulations, over the last screening and drug repurposing initiatives. Furthermore, few years some plug-ins were developed to facilitate them. other databases of compounds such as ZINC, available also Two PyMOL [13] plug-ins exist for docking simulations in AutoDock format, can be readily and easily plugged in. using AutoDock Vina [14]. The one from the Lill research group is restricted to a Linux environment and requires additional software installation [6], while the one origi- nally developed under Linux by [12] has been adapted for E. Di Muzio and D. Toti have contributed equally to this work. its use in a Windows environment, though apparently tested only on Windows XP. Other examples include AUDocker * Fabio Polticelli LE, an AutoDock Vina GUI available under Windows [11] [email protected] and focused on large scale virtual screening tasks, and Elena Di Muzio DOVIS 2.0, a parallel virtual screening tool for Linux clus- [email protected] ters based on AutoDock 4.0 [5]. An AutoDock Vina inter- Daniele Toti face for setting up docking simulations is also available as [email protected] part of the UCSF Chimera molecular visualization pro- 1 Department of Sciences, Roma Tre University, Rome, Italy gram [10], although it appears more suited to expert than Vol.:(0123456789)1 3 J Comput Aided Mol Des Fig. 1 DockingApp’s Initial settings panel novice users. Recently, another PyMOL plug-in, the NRG- details of the application are described in the following suite, has been described, which allows to perform dock- subsections. ing simulations in real time using FlexAID [2]. Of note, the latest PyMOL executables are commercial products and the Initial configuration only officially-supported approach for building and install- ing PyMOL from the source code is under an open-source DockingApp requires a minimal configuration effort, environment such as Linux. related to the selection of the number of CPU cores to be Here we present DockingApp, a freely-accessible, used for AutoDock Vina’s execution and the specification platform-independent application for setting up, perform- of the installation directory of MGLTools [1, 8], which is a ing and analyzing the results of docking simulations using free Python library available for most platforms (Windows, AutoDock Vina in a painless and extremely user-friendly Linux, OSX etc.) and is required for the software to run. way. The application comes with a pre-built library of The appropriate MGLTools distribution is already included more than 1400 ready-to-dock, FDA-approved drugs for for the respective operating system in DockingApp’s pack- virtual screening and drug repurposing initiatives. In addi- ages. This setup can be done via the “Initial settings” panel tion, other, much larger databases of small molecule com- (Fig. 1), which is automatically loaded at startup when the pounds can be easily plugged into DockingApp, such as the application is run for the first time, and can be recalled at a renowned ZINC database [4], available in pdbqt AutoDock later date as the user needs. The default value for the num- format at the URL http://zinc.docking.org/pdbqt/. ber of CPU cores is set at half of the detected cores on the system; besides, DockingApp tries to automatically detect the location of MGLTools’ installation directory on the sys- tem via a heuristic search, and if found, the corresponding Methodology field is populated with the identified directory. As briefly stated in the Introduction, DockingApp is born Execution of docking and virtual screening jobs as a user-friendly software application meant to allow a variety of differently-skilled users to perform docking sim- DockingApp provides the user with the possibility of ulations, with high confidence on the results produced and carrying out docking simulations on a given receptor, minimal effort for setup and configuration. The former fea- either against a single ligand (via the “Docking” panel) ture is guaranteed by relying upon the state-of-the-art dock- or a library of small molecules (via the “Virtual Screen- ing program AutoDock Vina, which is the “engine” used ing” panel). In the former case, the user needs to specify by DockingApp to carry out the actual docking simulation; the receptor and the ligand to be docked either as .pdb or the latter feature is provided by a user-friendly graphical .pdbqt files, whereas in the latter, instead of a single ligand, interface that on one hand hides all the complexity behind the user needs to select a folder containing the molecules AutoDock Vina’s usage, and on the other hand allows for the input receptor will be screened against in .pdbqt format a convenient browsing of the results both in tabular form (see Fig. 2). The automatic conversion of the input receptor and via a three-dimensional visualization of the receptor and ligand from the .pdb to the .pdbqt file format, required and the identified docking poses. All of this was made pos- by AutoDock Vina to run, is performed by DockingApp by sible by the development of a platform-independent graphi- using the prepare_receptor4.py and prepare_ cal user interface or “wrapper” (developed in Java), whose ligand4.py MGLTools scripts, respectively. purpose is to both acquire the user’s input and process the Regardless of the type of execution chosen, the user is docking results, and by a Python program that is launched given the chance to choose a “Grid type” and a “Docking/ “behind the scenes” and is responsible of interacting with VS type” (Fig. 2). As a matter of fact, the search-space grid the included AutoDock Vina in the user’s behalf. Further can be either automatically computed by the application 1 3 J Comput Aided Mol Des Fig. 2 Input panel for Dock- ingApp’s virtual screening execution Fig. 3 Input panel for DockingApp’s docking execution, where the name in the text fields and taking advantage of the autocomplete fea- user has chosen to manually specify the docking grid by selecting the tures provided for convenience. A similar subpanel is displayed when appropriate grid-bounding residues, as shown in the corresponding the user opts for a “flexible” docking, allowing the user to select the subpanel. Here, the user can select any number of residues, either by flexible residues browsing their list from the input receptor or by starting to type their to encompass the whole receptor molecule, as in a “blind of either the receptor’s structure or of the subset of residues docking” run, or manually specified by the user via the chosen by the user. selection of a set of grid-bounding residues. In the former Docking/virtual screening (VS) can be either “rigid” case DockingApp automatically calculates the grid center or “flexible”: in the latter case, the user needs to choose as the geometric center of all the atoms of the receptor’s the flexible residues of the input receptor. Specific panels structure. In the latter case the grid center is calculated in a are provided for the selection of the grid-bounding resi- similar manner using the set of atoms of the manually spec- dues and for the flexible residues, featuring comboboxes ified residues. Once the grid center is
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